Abstract

Structural equation modeling (SEM) studies commonly derive constructs and paths from the manually reviewed literature. Expert judgment remains essential, but incomplete coverage and undocumented selection decisions can make this stage difficult to evaluate. We therefore develop a theory-guided, AI-assisted procedure for generating SEM candidate models from systematic literature evidence. PICOC defines the scope, and queries identify the primary records. Backward citation searching adds foundational studies, whereas forward searching adds recent applications. Sentence-BERT and BERTopic are then used to examine semantic structure. Topic terms, representative documents, concept evidence, and theoretical criteria inform the mapping from topics to candidate constructs. The retained constructs are assigned possible SEM roles and assembled into candidate paths. The result is a documented front-end method for candidate model development, not an empirically validated SEM model.

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